Predictive analytics refers to the algorithmic process of analyzing a set of feature-value pairs, referred to herein as “examples,” in order to make predictions about future, or otherwise unknown, events. This process is applicable to many different types of data. For example, the features included in each example may be ordinal (in which case the value is a number), nominal (which case the value is a string), or Boolean (with values of true and false or 0/1). Thus, predictive analytics may be applied in almost any area in which knowledge of historical data is available. For example, in the business context, past customer behavior may be analyzed with predictive analytics to optimize existing customer relationship processes, identify unexpected opportunities, and anticipate future issues.
The primary tool for predictive analytics is a predictive model which identifies relationships between different features in the historical data. This model may be any structure that takes an example and applies an algorithm to produce a resultant output number or category. A predictive model may be a man-made model, a machine-generated model, or an ensemble model that takes the results of sub-models and combines these results. Once created, the predictive model may be applied to new conditions in order to predict or influence future, or otherwise unknown events through a process referred to herein as “scoring.” More specifically, scoring is the process of taking a predictive model, an example set of data points, and optionally additional parametric and other defining conditions or factors, and producing a resultant score for each provided example. The score is the prediction of the model for the supplied example.
Conventional predictive models employ scoring schemes which are relatively basic. Typically, the only information provided is a single probability of a particular event or behavior, or more generally, an output value corresponding to the supplied example. Thus, it is desirable to expand and enhance conventional scoring techniques by including other forms of analysis to include, for example, an identification of variable features that maximize or minimize a particular goal outcome, causal conditions for a particular resource, and/or feature attributes that optimize resource expenditure.